scholarly journals Change Detection of Urban Trees in MLS Point Clouds Using Occupancy Grids

2021 ◽  
Author(s):  
Philipp-Roman Hirt ◽  
Yusheng Xu ◽  
Ludwig Hoegner ◽  
Uwe Stilla
Keyword(s):  
Laser Scanning ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Point Clouds ◽  
Automated Analysis ◽  
Urban Trees ◽  
Change Analysis ◽  
Single Tree ◽  
Local Point ◽  
Occupancy Grids

AbstractTrees play an important role in the complex system of urban environments. Their benefits to environment and health are manifold. Yet, especially near streets, the traffic can be impaired by a limited clearance. Even injuries could be caused by breaking tree parts. Hence, it is important to capture the trees in the frame of a tree cadastre and to ensure regular monitoring. Mobile laser scanning (MLS) can be used for data acquisition, followed by an automated analysis of the point clouds acquired over time. The presented approach uses occupancy grids with a grid size of 10 cm, which enable the comparison of several epochs in three-dimensional space. Prior to that, a segmentation of single tree objects is conducted. After cylinder-based trunk localisation, closely neighboured tree crowns are separated using weights derived from local point densities. Therefore, changes for every single tree can be derived with regard to its parameters and its point cloud. The testing area is set along an urban street in Munich, Germany, using the publicly available benchmark data sets TUM-MLS-2016/2018. In the frame of the evaluation, tree objects are geo-referenced and mapped in 2D. The tree parameters height and diameter at breast height are derived. The geometric evaluation of the change analysis facilitates not only the acquisition of stock changes, but also the detection of shape changes for the tree objects.

A new drone laser scanning benchmark dataset for characterization of single-tree and forest biophysical properties

2021 ◽  
Author(s):  
Puliti Stefano ◽  
Grant D. Pears ◽  
Michael S. Watt ◽  
Edward Mitchard ◽  
Iain McNicol ◽  
...  
Keyword(s):  
Boreal Forests ◽  
Laser Scanning ◽  
Method Development ◽  
Three Dimensional ◽  
Point Clouds ◽  
Benchmark Dataset ◽  
Validation Dataset ◽  
Tree Structures ◽  
Single Tree ◽  
Individual Trees

<p>Survey-grade drone laser scanners suitable for unmanned aerial vehicles (UAV-LS) allow the efficient collection of finely detailed three-dimensional information of tree structures. This data type allows forests to be resolved into discrete individual trees and has shown promising results in providing accurate in-situ observations of key forestry variables. New and improved approaches for analyzing UAV-LS point clouds have to be developed to transform the vast amounts of data from UAV-LS into actionable insights and decision support. Many different studies have explored various methods for automating single tree detection, segmentation, parsing into different tree components, and measurement of biophysical variables (e.g., diameter at breast height). Despite the considerable efforts dedicated to developing automated ways to process UAV-LS data into useful data, current methods tend to be tailored to small datasets, and it remains challenging to evaluate the performance of different algorithms based on a consistent validation dataset. To fill this knowledge gap and to further advance our ability to measure forests from UAV-LS data, we present a new benchmarking dataset. This data is composed of manually labelled UAV-LS data acquired a number of continents and biomes which span tropical to boreal forests. The UAV-LS data was collected exclusively used survey-grade sensors such as the Riegl VUX and mini-VUX series which are characterized by a point density in the range 1 – 10 k points m<sup>2</sup>. Currently, such data represent the state-of-the-art in aerial laser scanning data. The benchmark data consists of a library of single-tree point clouds, aggregated to sample plots, with each point classified as either stem, branch, or leaves. With the objective of releasing such a benchmark dataset as a public asset, in the future, researchers will be able to leverage such pre-existing labelled trees for developing new methods to measure forests from UAV-LS data. The availability of benchmarking datasets represents an important driver for enabling the development of robust and accurate methods. Such a benchmarking dataset will also be important for a consistent comparison of existing or future algorithms which will guide future method development.</p>

scholarly journals Performance of parameter-domain and spatial-domain pole-like feature segmentation using single and multiple terrestrial laser scans

2014 ◽  
Vol XL-5 ◽  
pp. 349-355 ◽  
Author(s):  
Z. Lari ◽  
K. Al-Durgham ◽  
A. Habib
Keyword(s):  
Urban Areas ◽  
Laser Scanning ◽  
Wide Spectrum ◽  
Three Dimensional ◽  
Point Clouds ◽  
Spatial Domain ◽  
Parameter Domain ◽  
Registration Procedure ◽  
Local Point ◽  
Point Density

Terrestrial laser scanning (TLS) systems have been established as a leading tool for the acquisition of high density three-dimensional point clouds from physical objects. The collected point clouds by these systems can be utilized for a wide spectrum of object extraction, modelling, and monitoring applications. Pole-like features are among the most important objects that can be extracted from TLS data especially those acquired in urban areas and industrial sites. However, these features cannot be completely extracted and modelled using a single TLS scan due to significant local point density variations and occlusions caused by the other objects. Therefore, multiple TLS scans from different perspectives should be integrated through a registration procedure to provide a complete coverage of the pole-like features in a scene. To date, different segmentation approaches have been proposed for the extraction of pole-like features from either single or multiple-registered TLS scans. These approaches do not consider the internal characteristics of a TLS point cloud (local point density variations and noise level in data) and usually suffer from computational inefficiency. To overcome these problems, two recently-developed PCA-based parameter-domain and spatial-domain approaches for the segmentation of pole-like features are introduced, in this paper. Moreover, the performance of the proposed segmentation approaches for the extraction of pole-like features from a single or multiple-registered TLS scans is investigated in this paper. The alignment of the utilized TLS scans is implemented using an Iterative Closest Projected Point (ICPP) registration procedure. Qualitative and quantitative evaluation of the extracted pole-like features from single and multiple-registered TLS scans, using both of the proposed segmentation approaches, is conducted to verify the extraction of more complete pole-like features using multipleregistered TLS scans.

scholarly journals 3D Object Shape Reconstruction from Underwater Multibeam Data and Over Ground Lidar Scanning

2018 ◽  
Vol 25 (2) ◽  
pp. 47-56 ◽  
Author(s):  
Marek Kulawiak ◽  
Zbigniew Łubniewski
Keyword(s):  
Laser Scanning ◽  
Spatial Information ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Measurement Data ◽  
Point Clouds ◽  
Shape Reconstruction ◽  
Reconstruction Algorithms ◽  
Processing Scheme ◽  
3D Shape Reconstruction

Abstract The technologies of sonar and laser scanning are an efficient and widely used source of spatial information with regards to underwater and over ground environment respectively. The measurement data are usually available in the form of groups of separate points located irregularly in three-dimensional space, known as point clouds. This data model has known disadvantages, therefore in many applications a different form of representation, i.e. 3D surfaces composed of edges and facets, is preferred with respect to the terrain or seabed surface relief as well as various objects shape. In the paper, the authors propose a new approach to 3D shape reconstruction from both multibeam and LiDAR measurements. It is based on a multiple-step and to some extent adaptive process, in which the chosen set and sequence of particular stages may depend on a current type and characteristic features of the processed data. The processing scheme includes: 1) pre-processing which may include noise reduction, rasterization and pre-classification, 2) detection and separation of objects for dedicated processing (e.g. steep walls, masts), and 3) surface reconstruction in 3D by point cloud triangulation and with the aid of several dedicated procedures. The benefits of using the proposed methods, including algorithms for detecting various features and improving the regularity of the data structure, are presented and discussed. Several different shape reconstruction algorithms were tested in combination with the proposed data processing methods and the strengths and weaknesses of each algorithm were highlighted.

scholarly journals GACM: A Graph Attention Capsule Model for the Registration of TLS Point Clouds in the Urban Scene

2021 ◽  
Vol 13 (22) ◽  
pp. 4497
Author(s):  
Jianjun Zou ◽  
Zhenxin Zhang ◽  
Dong Chen ◽  
Qinghua Li ◽  
Lan Sun ◽  
...  
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Three Dimensional ◽  
Point Clouds ◽  
Autonomous Driving ◽  
Feature Descriptors ◽  
Local Point ◽  
Urban Scene ◽  
Pairwise Registration ◽  
Structure Complexity

Point cloud registration is the foundation and key step for many vital applications, such as digital city, autonomous driving, passive positioning, and navigation. The difference of spatial objects and the structure complexity of object surfaces are the main challenges for the registration problem. In this paper, we propose a graph attention capsule model (named as GACM) for the efficient registration of terrestrial laser scanning (TLS) point cloud in the urban scene, which fuses graph attention convolution and a three-dimensional (3D) capsule network to extract local point cloud features and obtain 3D feature descriptors. These descriptors can take into account the differences of spatial structure and point density in objects and make the spatial features of ground objects more prominent. During the training progress, we used both matched points and non-matched points to train the model. In the test process of the registration, the points in the neighborhood of each keypoint were sent to the trained network, in order to obtain feature descriptors and calculate the rotation and translation matrix after constructing a K-dimensional (KD) tree and random sample consensus (RANSAC) algorithm. Experiments show that the proposed method achieves more efficient registration results and higher robustness than other frontier registration methods in the pairwise registration of point clouds.

scholarly journals CLASSIFICATION OF MOBILE TERRESTRIAL LIDAR POINT CLOUD IN URBAN AREA USING LOCAL DESCRIPTORS

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 1117-1122 ◽  
Author(s):  
M. Zaboli ◽  
H. Rastiveis ◽  
A. Shams ◽  
B. Hosseiny ◽  
W. A. Sarasua
Keyword(s):  
Urban Area ◽  
Point Cloud ◽  
Laser Scanning ◽  
Three Dimensional ◽  
Point Clouds ◽  
Automated Analysis ◽  
Support Vector ◽  
Terrestrial Lidar ◽  
Local Descriptors ◽  
3D Point Clouds

Abstract. Automated analysis of three-dimensional (3D) point clouds has become a boon in Photogrammetry, Remote Sensing, Computer Vision, and Robotics. The aim of this paper is to compare classifying algorithms tested on an urban area point cloud acquired by a Mobile Terrestrial Laser Scanning (MTLS) system. The algorithms were tested based on local geometrical and radiometric descriptors. In this study, local descriptors such as linearity, planarity, intensity, etc. are initially extracted for each point by observing their neighbor points. These features are then imported to a classification algorithm to automatically label each point. Here, five powerful classification algorithms including k-Nearest Neighbors (k-NN), Gaussian Naive Bayes (GNB), Support Vector Machine (SVM), Multilayer Perceptron (MLP) Neural Network, and Random Forest (RF) are tested. Eight semantic classes are considered for each method in an equal condition. The best overall accuracy of 90% was achieved with the RF algorithm. The results proved the reliability of the applied descriptors and RF classifier for MTLS point cloud classification.

scholarly journals Measuring the Contribution of Leaves to the Structural Complexity of Urban Tree Crowns with Terrestrial Laser Scanning

2021 ◽  
Vol 13 (14) ◽  
pp. 2773
Author(s):  
Georgios Arseniou ◽  
David W. MacFarlane ◽  
Dominik Seidel
Keyword(s):  
Laser Scanning ◽  
Structural Complexity ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
Urban Trees ◽  
Box Dimension ◽  
State University ◽  
Leaf Removal ◽  
Quercus Macrocarpa ◽  
Methodological Considerations

Trees have a fractal-like branching architecture that determines their structural complexity. We used terrestrial laser scanning technology to study the role of foliage in the structural complexity of urban trees. Forty-five trees of three deciduous species, Gleditsia triacanthos, Quercus macrocarpa, Metasequoia glyptostroboides, were sampled on the Michigan State University campus. We studied their structural complexity by calculating the box-dimension (Db) metric from point clouds generated for the trees using terrestrial laser scanning, during the leaf-on and -off conditions. Furthermore, we artificially defoliated the leaf-on point clouds by applying an algorithm that separates the foliage from the woody material of the trees, and then recalculated the Db metric. The Db of the leaf-on tree point clouds was significantly greater than the Db of the leaf-off point clouds across all species. Additionally, the leaf removal algorithm introduced bias to the estimation of the leaf-removed Db of the G. triacanthos and M. glyptostroboides trees. The index capturing the contribution of leaves to the structural complexity of the study trees (the ratio of the Db of the leaf-on point clouds divided by the Db of the leaf-off point clouds minus one), was negatively correlated with branch surface area and different metrics of the length of paths through the branch network of the trees, indicating that the contribution of leaves decreases as branch network complexity increases. Underestimation of the Db of the G. triacanthos trees, after the artificial leaf removal, was related to maximum branch order. These results enhance our understanding of tree structural complexity by disentangling the contribution of leaves from that of the woody structures. The study also highlighted important methodological considerations for studying tree structure, with and without leaves, from laser-derived point clouds.

scholarly journals USING MOBILE LASER SCANNING DATA FOR FEATURES EXTRACTION OF HIGH ACCURACY DRIVING MAPS

2016 ◽  
Vol XLI-B3 ◽  
pp. 433-439 ◽  
Author(s):  
Bisheng Yang ◽  
Yuan Liu ◽  
Fuxun Liang ◽  
Zhen Dong
Keyword(s):  
Laser Scanning ◽  
Human Error ◽  
Traffic Accidents ◽  
Three Dimensional ◽  
Point Clouds ◽  
High Accuracy ◽  
3D Point Clouds ◽  
Assistant Systems ◽  
Novel Method ◽  
High Flexibility

High Accuracy Driving Maps (HADMs) are the core component of Intelligent Drive Assistant Systems (IDAS), which can effectively reduce the traffic accidents due to human error and provide more comfortable driving experiences. Vehicle-based mobile laser scanning (MLS) systems provide an efficient solution to rapidly capture three-dimensional (3D) point clouds of road environments with high flexibility and precision. This paper proposes a novel method to extract road features (e.g., road surfaces, road boundaries, road markings, buildings, guardrails, street lamps, traffic signs, roadside-trees, power lines, vehicles and so on) for HADMs in highway environment. Quantitative evaluations show that the proposed algorithm attains an average precision and recall in terms of 90.6% and 91.2% in extracting road features. Results demonstrate the efficiencies and feasibilities of the proposed method for extraction of road features for HADMs.

scholarly journals VISUALIZATION OF THE CONSTRUCTION OF ANCIENT ROMAN BUILDINGS IN OSTIA USING POINT CLOUD DATA

2017 ◽  
Vol XLII-2/W3 ◽  
pp. 345-352
Author(s):  
Y. Hori ◽  
T. Ogawa
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Feature Matching ◽  
Three Dimensional ◽  
Point Clouds ◽  
Final Report ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Coverage Accuracy ◽  
New Research

The implementation of laser scanning in the field of archaeology provides us with an entirely new dimension in research and surveying. It allows us to digitally recreate individual objects, or entire cities, using millions of three-dimensional points grouped together in what is referred to as "point clouds". In addition, the visualization of the point cloud data, which can be used in the final report by archaeologists and architects, should usually be produced as a JPG or TIFF file. Not only the visualization of point cloud data, but also re-examination of older data and new survey of the construction of Roman building applying remote-sensing technology for precise and detailed measurements afford new information that may lead to revising drawings of ancient buildings which had been adduced as evidence without any consideration of a degree of accuracy, and finally can provide new research of ancient buildings. We used laser scanners at fields because of its speed, comprehensive coverage, accuracy and flexibility of data manipulation. Therefore, we “skipped” many of post-processing and focused on the images created from the meta-data simply aligned using a tool which extended automatic feature-matching algorithm and a popular renderer that can provide graphic results.

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